http.Redirect(w, r, u.String(), http.StatusTemporaryRedirect)
				return
			}
		}
		h.ServeHTTP(w, r)
	})
}

var redirectPrefixes = map[string]struct{}{
	"favicon-16x16.png": {},
	"favicon-32x32.png": {},
	"favicon-96x96.png": {},
	"index.html":        {},
	minioReservedBucket: {},
}

// Fetch redirect location if urlPath satisfies certain
// criteria. Some special names are considered to be

	{name: "Mul64uhilo", argLength: 2, typ: "(UInt64,UInt64)", commutative: true}, // arg0 * arg1, returns (hi, lo)

	{name: "Mul32uover", argLength: 2, typ: "(UInt32,Bool)", commutative: true}, // Let x = arg0*arg1 (full 32x32-> 64 unsigned multiply), returns (uint32(x), (uint32(x) != x))
	{name: "Mul64uover", argLength: 2, typ: "(UInt64,Bool)", commutative: true}, // Let x = arg0*arg1 (full 64x64->128 unsigned multiply), returns (uint64(x), (uint64(x) != x))

^bb0(%arg0: tensor<1x224x224x3x!quant.uniform<u8:f32, 7.812500e-03:128>>):
  %cst = arith.constant dense<-1.23697901> : tensor<32xf32>
  %2 = "tfl.dequantize"(%arg0) : (tensor<1x224x224x3x!quant.uniform<u8:f32, 7.812500e-03:128>>) -> tensor<1x224x224x3xf32>
  %w = arith.constant dense<-1.0> : tensor<32x3x3x3xf32>

^bb0(%arg0: tensor<1x224x224x3x!quant.uniform<u8:f32, 7.812500e-03:128>>):
  %cst = arith.constant dense<-1.23697901> : tensor<32xf32>
  %2 = "tfl.dequantize"(%arg0) : (tensor<1x224x224x3x!quant.uniform<u8:f32, 7.812500e-03:128>>) -> tensor<1x224x224x3xf32>
  %w = arith.constant dense<-1.0> : tensor<32x3x3x3xf32>

  %2 = "tfl.pseudo_qconst"() {qtype = tensor<32x!quant.uniform<i32:f32, 1.7052092479439231E-4>>, value = dense<0> : tensor<32xi32>} : () -> tensor<32x!quant.uniform<i32:f32, 1.7052092479439231E-4>>

  %1 = "tf.XlaSharding"(%0) { _XlaSharding = "\01\02\03", sharding = "\01\02\03" } : (tensor<32xf32>) -> tensor<32xf32>
  %2 = "tf.ReadVariableOp"(%arg1) : (tensor<*x!tf_type.resource<tensor<32xf32>>>) -> tensor<32xf32>
  %3 = "tf.Identity"(%2) : (tensor<32xf32>) -> tensor<32xf32>
  %4 = "tf.XlaSharding"(%3) { _XlaSharding = "\04\05\06", sharding = "\04\05\06" } : (tensor<32xf32>) -> tensor<32xf32>

func.func @bias_add(%arg0: tensor<1x10x10x32xf32>, %arg1: tensor<32xf32>) -> tensor<1x10x10x32xf32> {
  %0 = "tf.BiasAdd"(%arg0, %arg1) {T = "tfdtype$DT_FLOAT", data_format = "NHWC"} : (tensor<1x10x10x32xf32>, tensor<32xf32>) -> tensor<1x10x10x32xf32>
  func.return %0 : tensor<1x10x10x32xf32>
// CHECK: %[[BIASADD_0:.*]] = "tf.BiasAdd"(%arg0, %arg1) <{data_format = "NHWC"}> {T = "tfdtype$DT_FLOAT"} : (tensor<1x10x10x32xf32>, tensor<32xf32>) -> tensor<1x10x10x32xf32>

// CHECK:           %[[VAL_2:.*]] = "tfl.pseudo_const"{{.*}}dense<0.000000e+00> : tensor<32xf32>
// CHECK:           %[[VAL_3:.*]] = "tfl.conv_2d"(%[[VAL_0]], %[[VAL_1]], %[[VAL_2]]) <{dilation_h_factor = 1 : i32, dilation_w_factor = 1 : i32, fused_activation_function = "NONE", padding = "VALID", stride_h = 1 : i32, stride_w = 1 : i32}> : (tensor<128x32x32x3xf32>, tensor<32x1x1x3xf32>, tensor<32xf32>) -> tensor<128x32x32x32xf32>

  // CHECK-DAG: %[[VAL1:.+]] = "tfl.pseudo_qconst"() <{qtype = tensor<32x!quant.uniform<{{.+}}>>, value = dense<0> : tensor<32xi32>}>
  // CHECK-DAG: %[[VAL2:.+]] = "tfl.dequantize"(%[[VAL0]]) : (tensor<32x3x3x3x!quant.uniform<{{.+}}>>)
  // CHECK-DAG: %[[VAL3:.+]] = "tfl.dequantize"(%[[VAL1]]) : (tensor<32x!quant.uniform<{{.+}})

  %b = arith.constant dense<0.0> : tensor<32xf32>
  %dc = "tfl.depthwise_conv_2d"(%arg0, %w, %b) {depth_multiplier = 4 : i32, dilation_h_factor = 1 : i32, dilation_w_factor = 1 : i32, fused_activation_function = "NONE", padding = "VALID", stride_h = 4 : i32, stride_w = 5 : i32} : (tensor<1x224x224x3xf32>, tensor<32x3x3x3xf32>, tensor<32xf32>) -> tensor<1x112x112x32xf32>
  func.return %dc : tensor<1x112x112x32xf32>